Evaluation of Predicted Protein–Protein Complexes by Binding Free Energy Simulations

2019 ◽  
Vol 15 (3) ◽  
pp. 2071-2086 ◽  
Author(s):  
Till Siebenmorgen ◽  
Martin Zacharias
Keyword(s):  
Free Energy ◽  
Protein Complexes ◽  
Binding Free Energy ◽  
Energy Simulations

scholarly journals New Tools for Conformational and Binding Free Energy Simulations

2019 ◽  
Vol 116 (3) ◽  
pp. 142a
Author(s):  
Giacomo Fiorin ◽  
Grace Brannigan ◽  
Jérôme Hénin
Keyword(s):  
Free Energy ◽  
Binding Free Energy ◽  
Energy Simulations

scholarly journals CHARMM-GUI Free Energy Calculator for Absolute and Relative Ligand Solvation and Binding Free Energy Simulations

2020 ◽  
Vol 16 (11) ◽  
pp. 7207-7218 ◽  
Author(s):  
Seonghoon Kim ◽  
Hiraku Oshima ◽  
Han Zhang ◽  
Nathan R. Kern ◽  
Suyong Re ◽  
...  
Keyword(s):  
Free Energy ◽  
Binding Free Energy ◽  
Energy Simulations

scholarly journals Determination of the Interfacial Water Content in Protein-Protein Complexes from Free Energy Simulations

2006 ◽  
Vol 90 (3) ◽  
pp. 841-850 ◽  
Author(s):  
Peter Monecke ◽  
Thorsten Borosch ◽  
Jürgen Brickmann ◽  
Stefan M. Kast
Keyword(s):  
Free Energy ◽  
Water Content ◽  
Protein Complexes ◽  
Interfacial Water ◽  
Energy Simulations

scholarly journals CHARMM-GUI Free Energy Calculator for Practical Ligand Binding Free Energy Simulations with AMBER

2021 ◽  
Vol 61 (9) ◽  
pp. 4145-4151
Author(s):  
Han Zhang ◽  
Seonghoon Kim ◽  
Timothy J. Giese ◽  
Tai-Sung Lee ◽  
Jumin Lee ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Energy Simulations

scholarly journals Mapping, Structure and Modulation of PPI

2021 ◽  
Vol 9 ◽  
Author(s):  
Elisa Martino ◽  
Sara Chiarugi ◽  
Francesco Margheriti ◽  
Gianpiero Garau
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Hot Spots ◽  
Molecular Level ◽  
Protein Complexes ◽  
Binding Free Energy ◽  
Detailed Knowledge ◽  
Protein Protein Interactions ◽  
Advanced Technologies ◽  
Successful Design

Because of the key relevance of protein–protein interactions (PPI) in diseases, the modulation of protein-protein complexes is of relevant clinical significance. The successful design of binding compounds modulating PPI requires a detailed knowledge of the involved protein-protein system at molecular level, and investigation of the structural motifs that drive the association of the proteins at the recognition interface. These elements represent hot spots of the protein binding free energy, define the complex lifetime and possible modulation strategies. Here, we review the advanced technologies used to map the PPI involved in human diseases, to investigate the structure-function features of protein complexes, and to discover effective ligands that modulate the PPI for therapeutic intervention.

scholarly journals Molecular Dynamics Free Energy Simulations Reveal the Mechanism for the Antiviral Resistance of the M66I HIV-1 Capsid Mutation

Viruses ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 920
Author(s):  
Qinfang Sun ◽  
Ronald M. Levy ◽  
Karen A. Kirby ◽  
Zhengqiang Wang ◽  
Stefan G. Sarafianos ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Drug Resistance ◽  
Free Energy ◽  
Energy Cost ◽  
Binding Free Energy ◽  
Resistance Mutation ◽  
Side Chain ◽  
Ligand Interaction ◽  
Energy Simulations ◽  
Hiv 1

While drug resistance mutations can often be attributed to the loss of direct or solvent-mediated protein−ligand interactions in the drug-mutant complex, in this study we show that a resistance mutation for the picomolar HIV-1 capsid (CA)-targeting antiviral (GS-6207) is mainly due to the free energy cost of the drug-induced protein side chain reorganization in the mutant protein. Among several mutations, M66I causes the most suppression of the GS-6207 antiviral activity (up to ~84,000-fold), and only 83- and 68-fold reductions for PF74 and ZW-1261, respectively. To understand the molecular basis of this drug resistance, we conducted molecular dynamics free energy simulations to study the structures, energetics, and conformational free energy landscapes involved in the inhibitors binding at the interface of two CA monomers. To minimize the protein−ligand steric clash, the I66 side chain in the M66I−GS-6207 complex switches to a higher free energy conformation from the one adopted in the apo M66I. In contrast, the binding of GS-6207 to the wild-type CA does not lead to any significant M66 conformational change. Based on an analysis that decomposes the absolute binding free energy into contributions from two receptor conformational states, it appears that it is the free energy cost of side chain reorganization rather than the reduced protein−ligand interaction that is largely responsible for the drug resistance against GS-6207.

ProAffiMuSeq: sequence-based method to predict the binding free energy change of protein–protein complexes upon mutation using functional classification

2019 ◽  
Author(s):  
Sherlyn Jemimah ◽  
Masakazu Sekijima ◽  
M Michael Gromiha
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Large Scale ◽  
Structural Information ◽  
Protein Complexes ◽  
Binding Free Energy ◽  
Complex Structure ◽  
External Validation ◽  
Functional Class ◽  
Supplementary Information

Abstract Motivation Protein–protein interactions are essential for the cell and mediate various functions. However, mutations can disrupt these interactions and may cause diseases. Currently available computational methods require a complex structure as input for predicting the change in binding affinity. Further, they have not included the functional class information for the protein–protein complex. To address this, we have developed a method, ProAffiMuSeq, which predicts the change in binding free energy using sequence-based features and functional class. Results Our method shows an average correlation between predicted and experimentally determined ΔΔG of 0.73 and mean absolute error (MAE) of 0.86 kcal/mol in 10-fold cross-validation and correlation of 0.75 with MAE of 0.94 kcal/mol in the test dataset. ProAffiMuSeq was also tested on an external validation set and showed results comparable to structure-based methods. Our method can be used for large-scale analysis of disease-causing mutations in protein–protein complexes without structural information. Availability and implementation Users can access the method at https://web.iitm.ac.in/bioinfo2/proaffimuseq/. Supplementary information Supplementary data are available at Bioinformatics online.

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